Maintenance of Generalized Association Rules for Record Deletion Based on the Pre-Large Concept

In the past, we proposed an incremental mining algorithm for maintenance of generalized association rules as new transactions were inserted. Deletion of records in databases is, however, commonly seen in real-world applications. In this paper, we thus attempt to extend our previous approach to solve this issue. The proposed algorithm maintains generalized association rules based on the concept of pre-large itemsets for deleted data. The concept of pre-large itemsets is used to reduce the need for rescanning original databases and to save maintenance costs. The proposed algorithm doesn't need to rescan the original database until a number of records have been deleted. It can thus save much maintenance time. Key-Words: data mining, generalized association rule, taxonomy, large itemset, pre-large itemset.